A build-prep error on Rowan's new DMG MORI printer would have meant canceling the job. Quantitative layer data let the team continue safely.

Rowan University's Digital Engineering Hub installed a new DMG MORI LASERTEC 30 SLM US metal 3D printer this summer. The first build did not go according to plan. A software output error caused the printer to lay down only contours for the first 60 layers, skipping the infill and support structures entirely.

Many operators would have canceled the job at that point. Continuing risked part protrusion, swelling, or a recoater crash that could damage the printer. But Rowan's team had a new tool: Phase3D's Fringe Inspection system, which measures the build surface layer by layer and generates quantitative heightmaps.

Together, Rowan and Phase3D set a clear stop condition. If the heightmaps showed abnormal swelling, powder coverage problems, or part protrusions, they would abort immediately. If the measurements stayed within bounds, they would let the build continue.

The transition from contour-only layers to bulk infill is normally a high-risk moment. Standard machine cameras can show that something looks different, but they cannot measure whether the surface is changing in a way that threatens the print. Fringe Inspection supplied calibrated height data throughout the transition. The measurements showed no swelling, no protrusions, no peeling, and healthy powder layers.

With that evidence, Rowan let the build run to completion. The part finished successfully.

The partnership gives students and researchers access to the same layer-by-layer heightmaps that production teams use. The goal is to support process understanding, anomaly detection, and qualification research. For metal additive manufacturing, where a single failed build can cost tens of thousands of dollars and weeks of machine time, having objective data at the moment of decision matters.

Rowan is one of several universities pairing real-time inspection hardware with new metal printers. As the industry pushes toward serial production, the ability to distinguish between a harmless process variation and a genuine failure is becoming a core requirement.

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